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Mengyu Wang

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7 papers

2 author rows

AIIM Journal 2026 Journal Article

TransFair: Transferring fairness from ocular disease classification to progression prediction

Min Shi
Leila Gheisi
Chee-Hung Henry Chu
Raju Gottumukkala
Yan Luo
Mengyu Wang
Xingquan Zhu

The use of artificial intelligence (AI) in automated disease classification significantly reduces healthcare costs and improves the accessibility of services. However, this transformation has given rise to concerns about the fairness of AI, which disproportionately affects certain groups, particularly patients from underprivileged populations. Recently, a number of methods and large-scale datasets have been proposed to address group performance disparities. Although these methods have shown effectiveness in disease classification tasks, they may fall short in ensuring fair prediction of disease progression, mainly because of limited longitudinal data with diverse demographics available for training a robust and equitable prediction model. In this paper, we introduce TransFair to enhance demographic fairness in progression prediction for ocular diseases. TransFair aims to transfer a fairness-enhanced disease classification model to the task of progression prediction with fairness preserved. First, we train a fairness-aware EfficientNet called FairEN using extensive data for ocular disease classification. Subsequently, this fair classification model is adapted to a fair progression prediction model through knowledge distillation, which minimizes the latent feature distances between classification and progression prediction models. We evaluate FairEN and TransFair for fairness-enhanced ocular disease classification and progression prediction using both two-dimensional (2D) and 3D retinal images. Extensive experiments and comparisons show that TransFair enhances group fairness in predicting ocular disease progression.

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NeurIPS Conference 2025 Conference Paper

SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing

Sung-Hoon Yoon
Minghan Li
Gaspard Beaudouin
Congcong Wen
Muhammad Rafay Azhar
Mengyu Wang

Rectified flow models have become a $\textit{de facto}$ standard in image generation due to their stable sampling trajectories and high-fidelity outputs. Despite their strong generative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues during editing often result in outputs that do not faithfully reflect the target prompt. Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion; however, these methods still yield suboptimal editing quality. In this work, we propose a flow decomposition-and-aggregation framework built upon an inversion-free formulation to address these limitations. Specifically, we semantically decompose the target prompt into multiple sub-prompts, compute an independent flow for each, and aggregate them to form a unified editing trajectory. While we empirically observe that decomposing the original flow enhances diversity in the target space, generating semantically aligned outputs still requires consistent guidance toward the full target prompt. To this end, we design a projection and soft-aggregation mechanism for flow, inspired by gradient conflict resolution in multi-task learning. This approach adaptively weights the sub-target velocity fields, suppressing semantic redundancy while emphasizing distinct directions, thereby preserving both diversity and consistency in the final edited output. Experimental results demonstrate that our method outperforms existing zero-shot editing approaches in terms of semantic fidelity and attribute disentanglement. The code is available at https: //github. com/Harvard-AI-and-Robotics-Lab/SplitFlow.

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JBHI Journal 2023 Journal Article

Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma

Min Shi
Anagha Lokhande
Mojtaba S. Fazli
Vishal Sharma
Yu Tian
Yan Luo
Louis R. Pasquale
Tobias Elze

Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e. g. , RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.

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NeurIPS Conference 2023 Conference Paper

SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion Process

Mengyu Wang
Henghui Ding
Jun Hao Liew
Jiajun Liu
Yao Zhao
Yunchao Wei

In this paper, we explore a principal way to enhance the quality of object masks produced by different segmentation models. We propose a model-agnostic solution called SegRefiner, which offers a novel perspective on this problem by interpreting segmentation refinement as a data generation process. As a result, the refinement process can be smoothly implemented through a series of denoising diffusion steps. Specifically, SegRefiner takes coarse masks as inputs and refines them using a discrete diffusion process. By predicting the label and corresponding states-transition probabilities for each pixel, SegRefiner progressively refines the noisy masks in a conditional denoising manner. To assess the effectiveness of SegRefiner, we conduct comprehensive experiments on various segmentation tasks, including semantic segmentation, instance segmentation, and dichotomous image segmentation. The results demonstrate the superiority of our SegRefiner from multiple aspects. Firstly, it consistently improves both the segmentation metrics and boundary metrics across different types of coarse masks. Secondly, it outperforms previous model-agnostic refinement methods by a significant margin. Lastly, it exhibits a strong capability to capture extremely fine details when refining high-resolution images. The source code and trained models are available at SegRefiner. git

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AAAI Conference 2021 Conference Paper

Continual Learning by Using Information of Each Class Holistically

Wenpeng Hu
Qi Qin
Mengyu Wang
Jinwen Ma
Bing Liu

Continual learning (CL) incrementally learns a sequence of tasks while solving the catastrophic forgetting (CF) problem. Existing methods mainly try to deal with CF directly. In this paper, we propose to avoid CF by considering the features of each class holistically rather than only the discriminative information for classifying the classes seen so far. This latter approach is prone to CF because the discriminative information for old classes may not be sufficiently discriminative for the new class to be learned. Consequently, in learning each new task, the network parameters for previous tasks have to be revised, which causes CF. With the holistic consideration, after adding new tasks, the system can still do well for previous tasks. The proposed technique is called Per-class Continual Learning (PCL). PCL has two key novelties. (1) It proposes a one-class learning based technique for CL, which considers features of each class holistically and represents a new approach to solving the CL problem. (2) It proposes a method to extract discriminative information after training to further improve the accuracy. Empirical evaluation shows that PCL markedly outperforms the state-of-the-art baselines for one or more classes per task. More tasks also result in more gains.

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ICRA Conference 2020 Conference Paper

Heart Rate Sensing with a Robot Mounted mmWave Radar

Peijun Zhao
Chris Xiaoxuan Lu
Bing Wang 0013
Changhao Chen
Linhai Xie
Mengyu Wang
Niki Trigoni
Andrew Markham

Heart rate monitoring at home is a useful metric for assessing health e. g. of the elderly or patients in post-operative recovery. Although non-contact heart rate monitoring has been widely explored, typically using a static, wall-mounted device, measurements are limited to a single room and sensitive to user orientation and position. In this work, we propose mBeats, a robot mounted millimeter wave (mmWave) radar system that provide periodic heart rate measurements under different user poses, without interfering in a users daily activities. mBeats contains a mmWave servoing module that adaptively adjusts the sensor angle to the best reflection pro le. Furthermore, mBeats features a deep neural network predictor, which can estimate heart rate from the lower leg and additionally provides estimation uncertainty. Through extensive experiments, we demonstrate accurate and robust operation of mBeats in a range of scenarios. We believe by integrating mobility and adaptability, mBeats can empower many down-stream healthcare applications at home, such as palliative care, post-operative rehabilitation and telemedicine.

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NeurIPS Conference 2020 Conference Paper

HRN: A Holistic Approach to One Class Learning

Wenpeng Hu
Mengyu Wang
Qi Qin
Jinwen Ma
Bing Liu

Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data. This paper proposes an entirely different approach based on a novel regularization, called holistic regularization (or H-regularization), which enables the system to consider the data holistically, not to produce a model that biases towards some features. Combined with a proposed 2-norm instance-level data normalization, we obtain an effective one-class learning method, called HRN. To our knowledge, the proposed regularization and the normalization method have not been reported before. Experimental evaluation using both benchmark image classification and traditional anomaly detection datasets show that HRN markedly outperforms the state-of-the-art existing deep/non-deep learning models.

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